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Abstract #4814

Exploring the potential of StyleGAN projection for quantitative maps from diffusion-weighted MR images

Daniel Güllmar1, Wei-Chan Hsu1,2, Stefan Ropele3, and Jürgen R. Reichenbach1,2
1Institute of Diagnostic and Interventional Radiology, Medical Physics Group, Jena University Hospital, Jena, Germany, 2Michael Stifel Center Jena for Data-Driven and Simulation Science, Jena, Germany, 3Division of General Neurology, Medical University Graz, Graz, Austria


Synthetic medical images can be generated with a StyleGAN and are indistinguishable from real data even by experts. However, the projection of real data via latent space onto a synthetic image shows clear deviations from the original (at least on the second image). This plays a major role especially when using GANs to perform tasks such as image correction (e.g. noise reduction), image interpolation or image interpretation by analyzing the latent space. Based on the results shown, it is highly recommended to perform an analysis of the projection accuracy before applying any of these applications.

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